house-pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- importing the dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\AHOR ISAAC\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
      "        4.9800e+00],\n",
      "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
      "        9.1400e+00],\n",
      "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
      "        4.0300e+00],\n",
      "       ...,\n",
      "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
      "        5.6400e+00],\n",
      "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
      "        6.4800e+00],\n",
      "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
      "        7.8800e+00]]), 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
      "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
      "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
      "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
      "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
      "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
      "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
      "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
      "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
      "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
      "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
      "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
      "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
      "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
      "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
      "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
      "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
      "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
      "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
      "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
      "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
      "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
      "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
      "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
      "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
      "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
      "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
      "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
      "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
      "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
      "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
      "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
      "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
      "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
      "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
      "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
      "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
      "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
      "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
      "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
      "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
      "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
      "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
      "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
      "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
      "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]), 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
      "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'), 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\", 'filename': 'boston_house_prices.csv', 'data_module': 'sklearn.datasets.data'}\n"
     ]
    }
   ],
   "source": [
    "# -- import the boston house price dataset\n",
    "house_price_dataset = sklearn.datasets.load_boston()\n",
    "print(house_price_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- load the dataset to pandas DataFrame\n",
    "house_price_dataframe = pd.DataFrame(house_price_dataset.data, columns=house_price_dataset.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
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       "      <td>0.538</td>\n",
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       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
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      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- 1st five records\n",
    "house_price_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- add the target (price) column to the DataFrame\n",
    "house_price_dataframe['price'] = house_price_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>LSTAT</th>\n",
       "      <th>price</th>\n",
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       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  price  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
       "2     17.8  392.83   4.03   34.7  \n",
       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- dimension : check the number of rows and columns \n",
    "house_price_dataframe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "price      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- check for missing values\n",
    "house_price_dataframe.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       price  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- statistical measures of the dataset \n",
    "house_price_dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- understanding the correlation between various features in the dataset\n",
    "correlation = house_price_dataframe.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -- construct a heatmap to understand the correlation\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size': 8}, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- split the data and the target\n",
    "X = house_price_dataframe.drop(['price'], axis=1)\n",
    "y = house_price_dataframe['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      24.0\n",
       "1      21.6\n",
       "2      34.7\n",
       "3      33.4\n",
       "4      36.2\n",
       "       ... \n",
       "501    22.4\n",
       "502    20.6\n",
       "503    23.9\n",
       "504    22.0\n",
       "505    11.9\n",
       "Name: price, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- split the data into training and testing data \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X\n",
      "Original : (506, 13), \n",
      "Train : (404, 13), \n",
      "Test : (102, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"X\")\n",
    "print(\"Original : {}, \\nTrain : {}, \\nTest : {}\".format(X.shape, X_train.shape, X_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y\n",
      "Original : (506,), \n",
      "Train : (404,), \n",
      "Test : (102,)\n"
     ]
    }
   ],
   "source": [
    "print(\"y\")\n",
    "print(\"Original : {}, \\nTrain : {}, \\nTest : {}\".format(y.shape, y_train.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- model training \n",
    "# -- XGBoost Regressor \n",
    "model = XGBRegressor()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23.147501  20.99463   20.090284  34.69053   13.903663  13.510157\n",
      " 21.998634  15.1940975 10.899711  22.709627  13.832816   5.592794\n",
      " 29.810236  49.99096   34.89215   20.607384  23.351097  19.23555\n",
      " 32.695698  19.641418  26.991022   8.401829  46.00729   21.708961\n",
      " 27.062933  19.321356  19.288303  24.809872  22.61626   31.70493\n",
      " 18.542515   8.697379  17.395294  23.700663  13.304856  10.492197\n",
      " 12.688369  25.016556  19.67495   14.902088  24.193798  25.007143\n",
      " 14.900281  16.995798  15.6009035 12.699232  24.51537   14.999952\n",
      " 50.00104   17.525454  21.184624  31.998049  15.613355  22.89754\n",
      " 19.325378  18.717896  23.301125  37.222923  30.09486   33.102703\n",
      " 21.00072   49.999332  13.405827   5.0280113 16.492886   8.405072\n",
      " 28.64328   19.499939  20.586452  45.402164  39.79833   33.407326\n",
      " 19.83506   33.406372  25.271482  50.001534  12.521657  17.457413\n",
      " 18.61758   22.602625  50.002117  23.801117  23.317268  23.087355\n",
      " 41.700035  16.119293  31.620516  36.069206   7.0022025 20.3827\n",
      " 19.996452  11.986318  25.023014  49.970123  37.881588  23.123034\n",
      " 41.292133  17.596548  16.305374  30.034231  22.860699  19.810343\n",
      " 17.098848  18.898268  18.96717   22.606049  23.141363  33.183487\n",
      " 15.010934  11.693824  18.78828   20.80524   17.99983   19.68991\n",
      " 50.00332   17.207317  16.404053  17.520426  14.593481  33.110855\n",
      " 14.508482  43.821655  34.939106  20.381636  14.655634   8.094332\n",
      " 11.7662115 11.846876  18.69599    6.314154  23.983706  13.084503\n",
      " 19.603905  49.989143  22.300608  18.930315  31.197134  20.69645\n",
      " 32.21111   36.15102   14.240763  15.698188  49.99381   20.423601\n",
      " 16.184978  13.409128  50.01321   31.602146  12.271495  19.219482\n",
      " 29.794909  31.536846  22.798779  10.189648  24.08648   23.710463\n",
      " 21.991894  13.802495  28.420696  33.181534  13.105958  18.988266\n",
      " 26.576572  36.967175  30.794083  22.77071   10.201246  22.213818\n",
      " 24.483162  36.178806  23.09194   20.097307  19.470194  10.786644\n",
      " 22.671095  19.502405  20.109184   9.611871  42.799637  48.794792\n",
      " 13.097208  20.28583   24.793974  14.110478  21.701134  22.217012\n",
      " 33.003544  21.11041   25.00658   19.122992  32.398567  13.605098\n",
      " 15.1145315 23.088867  27.474783  19.364998  26.487135  27.499458\n",
      " 28.697094  21.21718   18.703201  26.775208  14.010719  21.692347\n",
      " 18.372562  43.11582   29.081839  20.289959  23.680176  18.308306\n",
      " 17.204844  18.320065  24.393475  26.396057  19.094141  13.3019905\n",
      " 22.15311   22.185797   8.516214  18.894428  21.792608  19.331121\n",
      " 18.197924   7.5006843 22.406403  20.004215  14.412416  22.503702\n",
      " 28.53306   21.591028  13.810223  20.497831  21.898977  23.104464\n",
      " 49.99585   16.242056  30.294561  50.001595  17.771557  19.053703\n",
      " 10.399217  20.378187  16.49973   17.183376  16.70228   19.495337\n",
      " 30.507633  28.98067   19.528809  23.148346  24.391027   9.521643\n",
      " 23.886024  49.995125  21.167099  22.597813  19.965279  13.4072275\n",
      " 19.948694  17.087479  12.738807  23.00453   15.222122  20.604322\n",
      " 26.207253  18.09243   24.090246  14.105     21.689667  20.08065\n",
      " 25.010437  27.874954  22.92366   18.509727  22.190847  24.004797\n",
      " 14.788686  19.89675   24.39812   17.796036  24.556297  31.970308\n",
      " 17.774675  23.356768  16.134794  13.009915  10.98219   24.28906\n",
      " 15.56895   35.209793  19.605724  42.301712   8.797891  24.400295\n",
      " 14.086652  15.408639  17.301126  22.127419  23.09363   44.79579\n",
      " 17.776684  31.50014   22.835577  16.888603  23.925127  12.097476\n",
      " 38.685944  21.388391  15.98878   23.912495  11.909485  24.960499\n",
      "  7.2018585 24.696215  18.201897  22.489008  23.03332   24.260433\n",
      " 17.101519  17.805563  13.493165  27.105328  13.311978  21.913465\n",
      " 20.00738   15.405392  16.595737  22.301016  24.708412  21.422579\n",
      " 22.878702  29.606575  21.877811  19.900253  29.605219  23.407152\n",
      " 13.781474  24.454706  11.897682   7.2203646 20.521074   9.725295\n",
      " 48.30087   25.19501   11.688618  17.404732  14.480284  28.618876\n",
      " 19.397131  22.468653   7.0117908 20.602013  22.970919  19.719397\n",
      " 23.693787  25.048244  27.977154  13.393578  14.513882  20.309145\n",
      " 19.306028  24.095829  14.894031  26.382381  33.298378  23.61644\n",
      " 24.591206  18.514652  20.900269  10.406055  23.303423  13.092017\n",
      " 24.675085  22.582184  20.502762  16.820635  10.220605  33.81239\n",
      " 18.608067  49.999187  23.775583  23.909609  21.192276  18.805798\n",
      "  8.502987  21.50807   23.204473  21.012218  16.611097  28.100965\n",
      " 21.193024  28.419638  14.294126  49.99958   30.988504  24.991066\n",
      " 21.433628  18.975573  28.991457  15.206939  22.817244  21.765755\n",
      " 19.915497  23.7961   ]\n"
     ]
    }
   ],
   "source": [
    "# -- model evaluation \n",
    "# -- accuracy for prediction on training data \n",
    "training_data_pred = model.predict(X_train)\n",
    "print(training_data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared Error : 0.9999948236320982\n",
      "Mean Absolute Error : 0.0145848437110976\n"
     ]
    }
   ],
   "source": [
    "# -- R Squared Error \n",
    "score_1 = metrics.r2_score(y_train, training_data_pred)\n",
    "\n",
    "# -- Mean Absolute Error\n",
    "score_2 = metrics.mean_absolute_error(y_train, training_data_pred)\n",
    "\n",
    "print(\"R Squared Error : {}\".format(score_1))\n",
    "print(\"Mean Absolute Error : {}\".format(score_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -- visualize the actual prices and predicted prices\n",
    "plt.scatter(y_train, training_data_pred)\n",
    "plt.xlabel(\"Actual Prices\")\n",
    "plt.ylabel(\"Predicted Prices\")\n",
    "plt.title(\"Actual Price vs. Predicted Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20.288406  20.60247   30.9758    29.910797   8.624953  14.235525\n",
      " 23.523052  29.730324  31.97293   22.015892  27.946804  24.034412\n",
      " 20.771402  21.849985  12.024127  22.074675  18.487509  11.052485\n",
      "  8.141267  15.730722  23.163914  20.233883  35.700928  19.041637\n",
      " 15.129744  20.228016  47.067314  30.426474  32.729023  17.345776\n",
      " 17.889462  19.40687   30.769133  25.431686  12.05332   17.900238\n",
      " 11.031679  21.109531  20.64321   21.982754  25.016281  12.648555\n",
      " 31.042107   7.106914  18.685764  14.380013  34.902393  16.130005\n",
      " 32.98537   10.950912  27.335018  29.399937   7.0065055 35.398712\n",
      " 22.978228  18.582758  19.224293  20.003431  15.341814  22.38517\n",
      " 19.077105  21.955967  18.91404   32.77595   32.76772   24.36317\n",
      " 48.262913  23.385014  10.551914  22.664684  14.671612   8.881822\n",
      " 16.700302  17.221045  26.204681  26.826794  21.590458  21.61216\n",
      " 19.662537  23.533224  33.25156   17.105299  19.517323  29.344427\n",
      " 48.885273  35.723255  18.257914  24.461905  27.235567  21.319864\n",
      " 21.661198  20.753967  11.241813  36.715706  37.199463   9.123582\n",
      " 43.117516  34.55289   22.041529  16.860765  29.876814  23.332623 ]\n"
     ]
    }
   ],
   "source": [
    "testing_data_pred = model.predict(X_test)\n",
    "print(testing_data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared Error : 0.8711660369151691\n",
      "Mean Absolute Error : 2.2834744154238233\n"
     ]
    }
   ],
   "source": [
    "# -- R Squared Error \n",
    "score_1 = metrics.r2_score(y_test, testing_data_pred)\n",
    "\n",
    "# -- Mean Absolute Error\n",
    "score_2 = metrics.mean_absolute_error(y_test, testing_data_pred)\n",
    "\n",
    "print(\"R Squared Error : {}\".format(score_1))\n",
    "print(\"Mean Absolute Error : {}\".format(score_2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "17f5952bae32d6189ffd28db1c2e652ba93d39dc3225232bd5e9f888086e47ab"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}